Average Salary by State, 2024

Author

Gaston Sanchez

Published

May 20, 2025

1 Description

The data visualization of this module is based on the choropleth map that appears in the following Forbes’s article by Belle Wong:

Average Salary by State in 2024

https://www.forbes.com/advisor/business/average-salary-by-state/

Choropleth map of Average Salary by State in the U.S.

1.1 Details (datasheet)

Topic(s):
- Economics
- U.S.
- Salaries
Data:
- Size: small data
- Format: 2 data sets, one in CSV format, another in HTML
- Requires web scraping, assuming data from HTML
- Requires some cleansing (e.g. regex)
- Requires merging salary-data with map-data
Graphic:
- Type: choropleth map
- Styles: ggplot, interactive ggiraph, interactive leaflet
- Interactive: yes

1.2 R Packages

# packages
library(tidyverse)     # ecosystem of data science packages
library(rvest)         # for web scraping
library(sf)            # "simple features" (classes for maps)
library(rnaturalearth) # maps database
library(leaflet)       # web interactive maps
library(usmap)         # map of US states (includes Alaska and Hawaii)
library(ggiraph)       # make ggplots interactive

1.3 Other Sources

Related to the above article, there is a similar one in SoFi’s website titled Average US Salary by State by Jacqueline DeMarco (updated Jan 28, 2025)

https://www.sofi.com/learn/content/average-salary-in-us/

2 Data

Average Salary by State data is displayed in the above Forbes’ webpage. Unfortunately, this HTML table is not scrapable.

The good news is that the same data, plus the median salary, can be found in the following SoFi webpage:

https://www.sofi.com/learn/content/average-salary-in-us/

2.1 Data from Forbes

The data in Forbes is an HTML table. However, this HTML table is not scrapable. This is why are providing a data set (obtained by copy-paste from Forbes’ webpage) in the text file wage.tsv (Tab-Separated Value file). The data can be easily imported with the function read_tsv() as follows:

# import file (all columns as 'character' data)
wages = read_tsv("wage.tsv", col_types = "ccc")

head(wages)

# A tibble: 6 × 3
  State      `Annual Average Wage` `Average Hourly Wage`
  <chr>      <chr>                 <chr>                
1 Alabama    $50,620               $24.32               
2 Alaska     $66,130               $31.79               
3 Arizona    $58,620               $28.18               
4 Arkansas   $48,570               $23.35               
5 California $73,220               $35.20               
6 Colorado   $67,870               $32.63

2.2 Data from SoFi

Alternatively, you can also scrape the HTML table available in the SoFi webpage Average US Salary by State (by Jacqueline DeMarco, 03/28/24)

download.file(
  url = "https://www.sofi.com/learn/content/average-salary-in-us/", 
  destfile = "average-salary-in-us.html")

The interesting thing is that this table contains not just average salaries but also median salaries.

# import data
tbls = read_html("average-salary-in-us.html") |>
  html_table()

dat = tbls[[1]]

# data with average and median salaries
dat = dat |>
  mutate(Average = as.numeric(str_remove_all(Average, "[,$]")),
         Median = as.numeric(str_remove_all(Median, "[,$]")))

2.3 Data Preparation

The raw data is not quite ready to be crunched. We need to perform a few manipulations of Annual Average Wage values, dealing with strings:

wages = wages |>
  mutate(Average = as.numeric(str_remove_all(`Annual Average Wage`, "[$,]")))

wages

# A tibble: 50 × 4
   State       `Annual Average Wage` `Average Hourly Wage` Average
   <chr>       <chr>                 <chr>                   <dbl>
 1 Alabama     $50,620               $24.32                  50620
 2 Alaska      $66,130               $31.79                  66130
 3 Arizona     $58,620               $28.18                  58620
 4 Arkansas    $48,570               $23.35                  48570
 5 California  $73,220               $35.20                  73220
 6 Colorado    $67,870               $32.63                  67870
 7 Connecticut $69,310               $33.32                  69310
 8 Delaware    $62,260               $29.93                  62260
 9 Florida     $55,980               $26.91                  55980
10 Georgia     $58,000               $27.88                  58000
# ℹ 40 more rows

3 Graphs: Choropleth Maps

Let’s go over a series of plotting rounds, starting with a basic map, and then gradually adding more elements, and customizing its appearance to get as close as possible to our target visualization.

3.1 Map of USA from `"rnaturalearth"`

We begin by producing a first map of the U.S. This requires to get map data of the US territory—at the State level—via ne_states(). Notice that the returnclass argument must be set as returnclass = "sf" to guarantee that the return map object is of class "sf" (simple features).

# map data
usa_map = ne_states(
  country = "united states of america", 
  returnclass = "sf")

class(usa_map)

[1] "sf"         "data.frame"

As you can tell, the object usa_map is an object of class "sf". But it is also a data.frame object, i.e. a table. It contains a large number of columns. The most important columns—for our exploratory graphing purposes—are name (name of State), and the latitude-longitude coordinates in geometry.

names(usa_map)

  [1] "featurecla" "scalerank"  "adm1_code"  "diss_me"    "iso_3166_2"
  [6] "wikipedia"  "iso_a2"     "adm0_sr"    "name"       "name_alt"  
 [11] "name_local" "type"       "type_en"    "code_local" "code_hasc" 
 [16] "note"       "hasc_maybe" "region"     "region_cod" "provnum_ne"
 [21] "gadm_level" "check_me"   "datarank"   "abbrev"     "postal"    
 [26] "area_sqkm"  "sameascity" "labelrank"  "name_len"   "mapcolor9" 
 [31] "mapcolor13" "fips"       "fips_alt"   "woe_id"     "woe_label" 
 [36] "woe_name"   "latitude"   "longitude"  "sov_a3"     "adm0_a3"   
 [41] "adm0_label" "admin"      "geonunit"   "gu_a3"      "gn_id"     
 [46] "gn_name"    "gns_id"     "gns_name"   "gn_level"   "gn_region" 
 [51] "gn_a1_code" "region_sub" "sub_code"   "gns_level"  "gns_lang"  
 [56] "gns_adm1"   "gns_region" "min_label"  "max_label"  "min_zoom"  
 [61] "wikidataid" "name_ar"    "name_bn"    "name_de"    "name_en"   
 [66] "name_es"    "name_fr"    "name_el"    "name_hi"    "name_hu"   
 [71] "name_id"    "name_it"    "name_ja"    "name_ko"    "name_nl"   
 [76] "name_pl"    "name_pt"    "name_ru"    "name_sv"    "name_tr"   
 [81] "name_vi"    "name_zh"    "ne_id"      "name_he"    "name_uk"   
 [86] "name_ur"    "name_fa"    "name_zht"   "FCLASS_ISO" "FCLASS_US" 
 [91] "FCLASS_FR"  "FCLASS_RU"  "FCLASS_ES"  "FCLASS_CN"  "FCLASS_TW" 
 [96] "FCLASS_IN"  "FCLASS_NP"  "FCLASS_PK"  "FCLASS_DE"  "FCLASS_GB" 
[101] "FCLASS_BR"  "FCLASS_IL"  "FCLASS_PS"  "FCLASS_SA"  "FCLASS_EG" 
[106] "FCLASS_MA"  "FCLASS_PT"  "FCLASS_AR"  "FCLASS_JP"  "FCLASS_KO" 
[111] "FCLASS_VN"  "FCLASS_TR"  "FCLASS_ID"  "FCLASS_PL"  "FCLASS_GR" 
[116] "FCLASS_IT"  "FCLASS_NL"  "FCLASS_SE"  "FCLASS_BD"  "FCLASS_UA" 
[121] "FCLASS_TLC" "geometry"

With the "sf' object in place, we can pass it to ggplot() and its layer function geom_sf(). To get a map of the contiguous states, we adjust the latitude-longitude coordinates with coord_sf():

# basic map of USA (contiguous states)
ggplot() +
  geom_sf(data = usa_map) +
  coord_sf(xlim = c(-123, -67), ylim = c(25, 50))

3.2 Merging map-data with salary-data

So far we have a map of the US States. But what about the salary data? We need to merge (join) the table of the map usa_map with the table of average salary by state wages. This merging operation is done with inner_join(), indicating the names of the columns in both tables that have the States: name from table usa_map, and State from table wages

# merging map and wages data tables
usa_map_wages = inner_join(usa_map, wages, by = c("name" = "State"))

3.3 Version 1

With the merged data usa_map_wages, we can plot again a map of the US contiguous states, color coding the states with the Average values:

# version 1
usa_map_wages |>
  ggplot() +
  geom_sf(aes(fill = Average)) +
  coord_sf(xlim = c(-123, -67), ylim = c(25, 50))

3.4 Version 2

We need to change the colors in order to follow the scheme used in the choropleth map published in Forbes:

# version 2
inner_join(usa_map, wages, by = c("name" = "State")) |>
  ggplot() +
  geom_sf(aes(fill = Average)) +
  scale_fill_gradient(low = "#0B057A", high = "#E6E6F1") +
  coord_sf(xlim = c(-123, -67), ylim = c(25, 50))

3.5 Version 3

The next step involves categorizing salary values into different brackets

average salary greater than or equal to 70,000
average salary between 60,000 and 70,000
average salary between 50,000 and 60,000
average salary less than 50,000

Also, for sake of practice, we can change the color scheme (to pink):

# version 3
inner_join(usa_map, wages, by = c("name" = "State")) |>
  mutate(Cutoffs = case_when(
    Average >= 70000 ~ ">=70K",
    Average >= 60000 ~ "60K-70K",
    Average >= 50000 ~ "50K-60K",
    Average < 50000 ~ "<50K")) |>
  mutate(Color = case_when(
    Cutoffs == "<50K" ~ "#e6e6f1",
    Cutoffs == "50K-60K" ~ "#9D9BC9",
    Cutoffs == "60K-70K" ~ "#5450a1",
    Cutoffs == ">=70K" ~ "#0b057a"
  )) |>
ggplot() +
  geom_sf(aes(fill = Color)) +
  # I don't look like this palette
  scale_fill_manual(values = c("#0b057a", "#5450a1", "#9D9BC9", "#e6e6f1")) +
  coord_sf(xlim = c(-123, -67), ylim = c(25, 50))

3.6 Map with all States

What if we want to visualize all States (including Alaska and Hawaii) and not just the contiguous territory?

No problem! We can use one of the maps from the package "usmap", namely the map object returned by the function us_map().

The following code chunk starts with the merging operation to join us_map() data with the wages data, and the pipes it to ggplot() and friends. In addition, we are using a Viridis continuous scale color:

# use the data from "us_map()" which is already an "sf" object
us_map() |>
  inner_join(wages, by = c("full" = "State")) |>
  ggplot() +
  geom_sf(aes(fill = Average), color = "white") +
  scale_fill_viridis_c(direction = -1) +
  theme_void()

We can make it interactive by using geom_sf_interactive() and activating the tooltip argument inside the aesthetic mappings.

# use the data from "us_map()" which is already an "sf" object
gmap = us_map() |>
  inner_join(wages, by = c("full" = "State")) |>
  ggplot() +
  geom_sf_interactive(aes(fill = Average, 
                          tooltip = paste0(full, ": ", Average)), 
                      color = "white") +
  scale_fill_viridis_c(direction = -1) +
  theme_void()

girafe(ggobj = gmap)

3.7 Interactive Map with Leaflet

The previous maps have been produced with ggplot. They are nice but are static visualizations. Interestingly, we can use the "leaflet" package that allows us graph interactive web maps.

inner_join(usa_map, wages, by = c("name" = "State")) |>
  mutate(Cutoffs = case_when(
    Average >= 70000 ~ ">=70K",
    Average >= 60000 ~ "60K-70K",
    Average >= 50000 ~ "50K-60K",
    Average < 50000 ~ "<50K")) |>
  mutate(Color = case_when(
    Cutoffs == "<50K" ~ "#e6e6f1",
    Cutoffs == "50K-60K" ~ "#9D9BC9",
    Cutoffs == "60K-70K" ~ "#5450a1",
    Cutoffs == ">=70K" ~ "#0b057a"
  )) |>
  mutate(Label = paste0(name, ", $", Average)) |>
leaflet() |>
  addTiles() |>
  setView(lng = -100, 
          lat = 40, 
          zoom = 4) |>
  addPolygons(color = ~Color,
              fillColor = ~Color, 
              fillOpacity = 0.8, 
              label = ~Label,
              weight = 1,
              highlight = highlightOptions(
                weight = 5,
                color = "#000000",
                fillOpacity = 1))